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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Spectral clustering based on the local similarity measure of shared neighbors

  • Cao, Zongqi (Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University) ;
  • Chen, Hongjia (Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University) ;
  • Wang, Xiang (Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University)
  • Received : 2021.07.23
  • Accepted : 2022.02.05
  • Published : 2022.10.10
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Abstract

Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement exploits the local density information between data points based on the weight of the shared neighbors in a directed k-nearest neighbor graph with only one parameter k, that is, the number of nearest neighbors. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering performance measured via the normalized mutual information, clustering accuracy, and F-measure. As an example, the proposed method can provide an improvement of 15.82% in the clustering performance for the Soybean dataset.

Keywords

Acknowledgement

Thank the anonymous reviewers for very helpful comments and suggestions. The work is supported by the National Natural Science Foundation of China under Frant Nos. 11961048, 12001262, and 11801258. The work is supported by Jiangxi Provincial Natural Science Foundation under Grant No. 20181ACB20001.

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